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All Journal TIN: TERAPAN INFORMATIKA NUSANTARA Industrial Innovation
Jamila Jamila
Politeknik ATK Yogyakarta
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Modeling of women shoes sizing system based on 3D foot scanner result using machine learning approach Jamila Jamila; Eka Legya Frannita; Gilang Fatikhul Burhan; Windra Bangun Nuswantoro; Anwar Hidayat; Erlita Pramitaningrum; Totok Yulaidin
Industrial Innovation Vol. 2 No. 2 (2025): Industrial Innovation
Publisher : Politeknik ATK Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58533/aqn1f925

Abstract

Accurate shoe sizing plays a crucial role in ensuring comfort, performance, and consumer satisfaction, particularly for women whose foot shapes exhibit considerable anatomical variability. To address this challenge, this research proposes a data-driven modeling framework for developing a women’s shoe sizing system based on three-dimensional foot scanner data. The study was carried out through a systematic process consisting of data preprocessing, clustering using the K-Means algorithm, and evaluation of the clustering performance. The clustering analysis identified four optimal clusters within the dataset, representing distinct patterns in foot dimension measurements. The evaluation result, with a Silhouette Score of 0.25, indicates a moderate yet acceptable level of cohesion and separation among the clusters. These findings demonstrate that the proposed model can effectively capture the underlying structure of women’s foot morphology, providing a scientific foundation for establishing more accurate, customized, and ergonomically appropriate shoe sizing standards.
Deteksi Defect Sepatu Berbasis Fine-Tuned CNN Menggunakan Arsitektur DenseNet untuk Mendukung Proses Quality Control Jamila Jamila; Eka Legya Frannita; Gilang Fatikhul Burhan; Nunik Purwaningsih; Windra Bangun Nuswantoro; Anwar Hidayat; Erlita Pramitaningrum; Totok Yulaidin; Alifia Revan Prananda
TIN: Terapan Informatika Nusantara Vol 7 No 3 (2026): August 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i3.9712

Abstract

Indonesia is a leading global exporter of footwear. Despite successfully exports million pair of shoes, shoes industry in Indonesia faces significant challenges due to conventional quality control (QC) limitations. This study proposes a deep learning-based defect detection model to enhance the efficiency and accuracy of the QC process. This study uses primary datasets collected from Laboratory of PIDI 4.0 Politeknik ATK Yogyakarta and employed in four experimental scenarios. The dataset consists of 2,828 images comprising of two classes (good class and reject class). This study utilizes fine-tuned CNN using DenseNet architecture for distinguishing two classes. Before employing the fine-tuned CNN method, we separate the data into three components, which are training, validation and testing data, with proportion of 80:10:10. The results demonstrate that the integration of data augmentation and the Adam optimizer yielded the highest performance, achieving accuracy, precision, recall and F1-score of 0.9546, 0.9551, 0.9546, 0.9545, respectively. These findings suggest that implementing automated deep learning models has potential to reduce rejection rates by modernizing traditional inspection methods, which can strengthen the competitiveness of Indonesia’s footwear industry in the global market.
Co-Authors Alifia Revan Prananda Anwar Hidayat Anwar Hidayat Eka Legya Frannita Erlita Pramitaningrum Gilang Fatikhul Burhan Nunik Purwaningsih Totok Yulaidin Totok Yulaidin Windra Bangun Nuswantoro